>> Two hundred years ago,
we lived in extreme poverty.
We had very little medical knowledge.
As a result, our average life expectancy was 32 years.
A hundred years went by and very little progress happened.
Life expectancies of 34 years.
This continues on until the year 1918,
when life expectancy actually drops to 23 years.
Initially, I thought this was an anomaly in the data,
but it turns out to be the impact of the Spanish flu.
The Spanish flu infected 27 percent of the world's population,
killing between 50-100 million people.
This is an insight that is very relevant to our world today and
serves as a reminder of how devastating a global pandemic can be.
The Spanish flu is followed by the discovery of penicillin,
the production of antibiotics,
and by the 1960s,
average life expectancy jumps to 55 years.
But this masks a huge global health inequality.
A country such as Norway have life expectancy in the 70s,
where other developing countries,
such as Sierra Leone,
have life expectancy in the 30s.
This brings us to today.
Let's just take a moment to
celebrate the amazing journey we have gone on.
Life expectancy is 73 years and
the health inequality gap around the world is narrowing,
with the biggest improvements taking place in
the developing world in the last 50 years.
In the US, we can see that
the average life expectancy is at 79 years.
If we select the US,
we can uncover some insightful local trends,
such as this fall in life expectancy during the Civil War.
What happened in these last couple of decades that has
improved life expectancy so much?
Well one really important factor to consider is child mortality.
In this chart, we are comparing
life expectancy to child mortality.
We can clearly see that countries that have
higher life expectancy rates
also have lower child mortality rates.
But just 30 years ago,
we had countries where
one in three children were dying before the age of five.
Let's take a look at how things have actually
changed in these past 30 years.
Now as our dot start to move,
take a look to Rwanda here that saw this awful increase in
child mortality rates caused by the Rwandan genocide.
But generally the trends are for the positive.
We're seeing countries starting to
reduce their child mortality rates,
increased their life expectancy.
We see another anomaly here that was
actually caused by the Haiti earthquake.
But in a matter of 30 years,
we see countries are able to reduce
their child mortality rate from one in three to one in 10.
I can now start drilling into individual country journeys.
Let's take another look at Sierra Leone over here,
where we can see child mortality rates have reduced massively.
During the same time period,
we can see significant improvements in GDP
per capita and immunizations rate,
helping us piece together
the transformation the country went through.
Building a visual in Power BI is as
simple as asking a natural language question.
We're going to start by looking at life expectancy by
sex and adding a visualization to the page.
We're going to add another one,
looking at life expectancy by year.
Power BI automatically figures out
the most appropriate visualizations for my questions,
which is a bar chart and a line chart.
Let's add some enrichments to
my line chart and try and predict how
long people are going to be living by the year 2100.
It's very easy for me to add a forecast to this visualization.
I can choose how many points I want to forecast into the future,
I can choose things like my confidence intervals,
whether I want to apply seasonality and I
can just add this enrichment to the visualization.
I can see by the year 2100,
we're going to be living to almost 90 years old.
But because I was able to build these visualizations so quickly,
I'm actually going to add another visualization.
This time I'm going to add an AI visualization to the page,
which is going to help me do key driver analysis.
Now we have hundreds of metrics we
could be analyzing in this dataset.
We're going to be focusing on our socioeconomic factors.
The question we're going to be asking is,
what actually influenced this life expectancy to increase?
Now every time I drag in one of these factors from my dataset,
we're actually running a machine learning model behind the scenes.
Every time another factor gets added,
we're actually rerunning this model,
throwing away the old one and
calculating a completely new one from scratch.
As we add these factors,
we can see the top factor that comes up is safe drinking water.
When more people have access to
safe drinking water in the population,
our life expectancy increases.
But what if we're only interested in the female population?
We'll, I can simply select "females" in my bar chart over
here and the whole machine learning model just runs from scratch.
Now when it comes to females,
my top factor is actually education.
As the percentage of females who goes to school decreases,
so does the life expectancy.
I can also look at drivers at a country level.
All I have to do is type in a country name,
Great Britain over here,
and now we're going to rerun
the whole model just for Great Britain.
Here we can see the top factor is actually GDP per capita.
As GDP per capita increases,
so does life expectancy.
But what if I want to do some variance analysis?
For example, I might want to take a look at where
life expectancy increased the most since 2010.
The decomposition tree in Power BI enables
me to break down any metric I want in any order.
I can start by breaking things down by continent and I can
very quickly see that life expectancy increased the most in Asia.
But what actually drove this increase?
Here we have a lot of different factors to
consider and I might not be sure where I should be looking.
Here the AI and Power BI can help me figure this out.
I can simply ask Power BI where this increase is coming from,
and Power BI is going to analyze all of
these factors and tell me that countries in Asia that
saw significant decreases in
maternal mortality saw the largest increases in life expectancy,
a growth of 7.56 percent on average.
In the case of Africa,
the AI reruns and tells us that the increase in
life expectancy was associated with a decrease in malaria cases,
whereas for Europe, it was to do
with an increase in GDP per capita.
I can keep going down and doing this root cause analysis,
asking Power BI to keep finding the high values for me.
At any point I can drill into this path and
actually see which countries fall into this segment.
In this case, we can see
all our southern and eastern European countries come up.
These are all important insights that
I want to be able to share with my team.
I can simply save these as bookmarks with
annotations that contain my conclusions,
that I can go ahead and present at my next team meeting.
I can even bookmark more complex things,
such as paths inside my decomposition tree,
as well as data selections.
Here I have a website full of
awesome flag data that I want to use as a data source.
The only problem is it's not in a nicely structured table,
so it's not easy for me to pull this.
Luckily, we also have AI for data prep in Power BI that can help.
First thing we need to do is connect to some data,
and Power BI has a ton of connectors,
all the way from Access database to Zucchetti HR Infinity.
But we actually want to use over here,
is our web connector.
The first thing that we need to do,
is we need to connect to our flag website.
As we've established, we do not have a nicely structured table.
So what we're going to do instead is we're actually going to add
this data using examples that we can just give to Power BI,
and Power BI will just figure out the state.
First thing I'm going to do is give thing a column,
and then we're just going to give Power BI a couple of examples.
So we're going to type in Afghanistan,
we're going to type in Albania. Here we go.
Power BI just figures out the rest. Let's do another one.
Let's maybe do population.
Over here, again, we're going to type in
the first example that we see under Afghanistan.
Now Power BI just needs simply one example to figure this out.
I don't have to go in order. I can skip a couple.
I can go into density,
type this one as well.
Again, Power BI just figures this out.
It has never been easier to scrape data from the web.
I'm just going to wait for this table to
load up, and we have this data.
But still not looking super clean.
What I really want is my country name to be in a separate column.
So again, I could use the hundreds
of out of the box functions that we have inside Power BI,
but I'm just going to add a column from example.
I'm going to type in Afghanistan over here.
Power BI just figures out I don't want the country code.
I can just add that as a column.
Now we see over here
a nice preview of the distribution of my data,
if we have any blanks or errors that might be there.
Just like I was able to add this qualitative field,
I can also really easily
manipulate my quantitative data using examples.
Over here we have our life expectancy column,
and then what I want to actually do is split it up into ranges.
Instead of having 81, I want it to be between 80 and 85.
Again, Power BI just figures out here we see 79,
75 to 80 figures out the pattern that I want.
For joining files, I can easily join them over here,
and I can specify which tables I want to join,
which columns I want to join them on,
what kind of join I want to do and whether I want to do things
like fuzzy matching if it's not going to be an exact match.
But I don't need to flatten everything out.
I can keep my data in a star schema,
and Power BI gives you a complete semantic model of
relationships, hierarchies and calculations.
You can even auto
detect relationships based in the columns and the underlying data.
With Power BI, I can of course,
subscribe my business users
to regular newsletter about their data.
Or I can set up alerts for
notifications when their data crosses a particular threshold.
However, all of my business users spend their day within Teams.
So to truly maximize productivity,
we need to bring the data to the users.
So instead of accessing all my Power BI content in the web,
we now have a fully integrated and rich app experience
built into Teams.
Here I can access my reports,
my dashboards, consume content and even set up alerts,
but I can also simply collaborate on my data
directly inside my Teams channel
where the rest of the group works.
Here I have shared a Power BI report,
which is just shown as another tab inside my Teams channel.
This is not a screenshot of my data.
This is a fully interactive Power BI report.
This makes it super easy for me to
consume and discuss the data as well as address
any comments that I have been tagged in
directly in Teams without any context switching.
We can see that this report has some important metadata
telling us that this is
confidential data restricted to my organization.
This is because Power BI uses
Microsoft Cloud App Security and is fully
integrated with its information protection capabilities.
To do some further analysis of my data for the search in general,
I may want to work with my data in Excel.
Now when I export out this data to Excel,
you're going to see that
the sensitivity labels get pass through Power BI,
through Teams, all the way to Excel.
My data is now encrypted,
meaning if I leave the data on
a USB stick or I leave organization,
no one would be able to access it.
Finally, wouldn't my business users just want to do
some quick ad hoc analysis in Excel.
Well, Excel, now has
an inherent built-in understanding
of Power BI, organizational entities.
So the search in general can simply tell Excel that he
wants these countries to be treated as organizational data.
Look what happens.
We were able to automatically
create these organizational entities,
and I can even see
this Power BI entity card that describes all the data for me.
I can very easily pull this data into a table and you can create
some quick comparisons looking at things like
life expectancy or GDP per capita.
If I want to add another country,
I can just simply type that in,
and the Power BI takes a second to look at
this data and pulls it in from the data model as well.
This isn't a pivot table.
If I want to just edit these cells,
maybe do things like format this into a currency,
or even more complicated things like typing
in the formula where we're going to pull out, say,
the population, maybe we can multiply it by the GDP per
capita and create our own calculations on top of this data.
This is all live connected data to Power BI.
I can simply refresh it,
and if new data come in,
all of these numbers would update.
A top focus for Power BI has been
storytelling and improving the data literacy of every user.
For this reason, it is now as simple
as right-clicking a visualization and
asking Power BI to summarize
it to get all the top insights that we need.
Power BI is going to automatically analyze
the data and generate a summary for me.
Here, for example, we are looking at time series data.
Power BI automatically carries out to trend analysis,
highlighting the longest period of
continued growth occurred in North America,
where GDP grew over $15,000.
Europe, on the other hand, had more spikes and dips in the data.
So we just highlighted the last three years where we
saw an increase of GDP of almost 12 percent.
But what if I wanted insights like
these for the whole report page,
rather than just focusing on a single visual.
I can simply add the smart narratives visualization,
which is going to automatically analyze a whole report page,
figure out everything that's going on that's
important and just generate an automatic narrative for me,
summarizing everything that's going on.
This summary is fully customizable.
If I want to go ahead and maybe change around things like,
make some things bold or change the colors,
I can really easily do that.
It's just like making edits in any text box.
Not only can I change the formatting over here,
I can even add my own custom summaries as well.
For example, I want to type something like
average life expectancy since the year 2000,
I can just type that in.
But now I want to actually create a dynamic value
rather than just inputting a static number.
What we've actually done is instead of having to
write DAX to create your complex calculations,
you can just simply leverage Q & A and use natural language.
What I'm going to do now,
is I'm going to simply type in
average life expectancy since the year 2000,
just in natural language.
In Q & A instead of generating a visual,
is actually going to just generate a value for me,
do this complex calculation,
and I can just save this directly inside
my visualization and have it automatically get calculated,
truly democratizing, creating calculations for everyone.
This summary is of course,
fully interactive with the rest of my report as well.
For example, if I want to just focus on insights about Europe,
I can just select Europe over here and
my visualizations are going to cross-filter,
but so does my summary.
This applies to both the automatically generated summary,
as well as any additions that I've made,
including my dynamic values.
If I want to, for example,
do a comparison of Europe versus North America,
I can just select both of these and
my automatically generated summary
is just going to do this comparison for me.
It's never been easier to do storytelling with your data.